Pattern Of Lung Adenocarcinoma Research Articles

Characterization of polyamine metabolism predicts prognosis, immune profile, and therapeutic efficacy in lung adenocarcinoma patients.

Polyamine modification patterns in lung adenocarcinoma (LUAD) and their impact on prognosis, immune infiltration, and anti-tumor efficacy have not been systematically explored. Patients from The Cancer Genome Atlas (TCGA) were classified into subtypes according to polyamine metabolism-related genes using the consensus clustering method, and the survival outcomes and immune profile were compared. Meanwhile, the geneCluster was constructed according to the differentially expressed genes (DEGs) of the subtypes. Subsequently, the polyamine metabolism-related score (PMRS) system was established using the least absolute shrinkage and selection operator (LASSO) multivariate regression analysis in the TCGA training cohort (n = 245), which can be applied to characterize the prognosis. To verify the predictive performance of the PMRS, the internal cohort (n = 245) and the external cohort (n = 244) were recruited. The relationship between the PMRS and immune infiltration and antitumor responses was investigated. Two distinct patterns (C1 and C2) were identified, in which the C1 subtype presented an adverse prognosis, high CD8+ T cell infiltration, tumor mutational burden (TMB), immune checkpoint, and low tumor immune dysfunction and exclusion (TIDE). Furthermore, two geneClusters were established, and similar findings were observed. The PMRS, including three genes (SMS, SMOX, and PSMC6), was then constructed to characterize the polyamine metabolic patterns, and the patients were divided into high- and low-PMRS groups. As confirmed by the validation cohort, the high-PMRS group possessed a poor prognosis. Moreover, external samples and immunohistochemistry confirmed that the three genes were highly expressed in tumor samples. Finally, immunotherapy and chemotherapy may be beneficial to the high-PMRS group based on the immunotherapy cohorts and low half-maximal inhibitory concentration (IC50) values. We identified distinct polyamine modification patterns and established a PMRS to provide new insights into the mechanism of polyamine action and improve the current anti-tumor strategy of LUAD.

Prevalence of epidermal growth factor receptor (EGFR) mutations and correlation with histological patterns in lung adenocarcinoma in patients from Bosnia and Herzegovina

Prevalence of epidermal growth factor receptor (EGFR) mutations and correlation with histological patterns in lung adenocarcinoma in patients from Bosnia and Herzegovina

Clinicopathological significances of cribriform pattern in lung adenocarcinoma

The present study aimed to investigate the clinicopathological and prognostic implications of the cribriform pattern in lung adenocarcinoma through a meta-analysis. The estimated rates of cribriform pattern in lung adenocarcinomas were investigated. The correlations between cribriform pattern and clinicopathological characteristics, including genetic alterations and prognosis were evaluated. The estimated rate of cribriform pattern was 0.150 (95% confidence interval [CI], 0.101–0.218) in lung adenocarcinoma. The estimated rates of cribriform pattern in the 5% and 10% criteria were 0.230 (95% CI 0.125–0.386) and 0.130 (95% CI 0.062–0.252), respectively. The presence of cribriform pattern was significantly correlated with larger tumor size (> 30 mm), spread through air spaces, and lymph node metastasis (P < 0.001, P < 0.001, and P = 0.007, respectively, in the meta-regression test). There were no significant differences between cribriform pattern, smoking history, and vascular and lymphatic invasion. In lung adenocarcinoma with cribriform pattern, the estimated rates of ALK rearrangement, KRAS, and EGFR mutations were 0.407 (95% CI 0.165–0.704), 0.330 (95% CI 0.117–0.646), and 0.249 (95% CI 0.125–0.437), respectively. ALK rearrangement was significantly more frequent in lung adenocarcinomas with cribriform pattern than in those without. The overall survival rate was significantly worse in lung adenocarcinomas with a cribriform pattern than in those without (hazard ratio 2.051, 95% CI 1.369–3.075). In conclusion, the presence of a cribriform pattern can be a useful predictor of the clinicopathological characteristics and prognosis of patients with lung adenocarcinoma.

Molecular subtypes of lung adenocarcinoma patients for prognosis and therapeutic response prediction with machine learning on 13 programmed cell death patterns.

Lung adenocarcinoma (LUAD) seriously threatens people's health worldwide. Programmed cell death (PCD) plays a critical role in regulating LUAD growth and metastasis as well as in therapeutic response. However, currently, there is a lack of integrative analysis of PCD-related signatures of LUAD for accurate prediction of prognosis and therapeutic response. The bulk transcriptome and clinical information of LUAD were obtained from TCGA and GEO databases. A total of 1382 genes involved in regulating 13 various PCD patterns (apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, alkaliptosis and disulfidptosis) were included in the study. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to identify PCD-associated differential expression genes (DEGs). An unsupervised consensus clustering algorithm was used to explore the potential subtypes of LUAD based on the expression profiles of PCD-associated DEGs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF) analysis and stepwise multivariate Cox analysis were performed to construct a prognostic gene signature. The "oncoPredict" algorithm was utilized for drug-sensitive analysis. GSVA and GSEA were utilized to perform function enrichment analysis. MCPcounter, quanTIseq, Xcell and ssGSEA algorithms were used for tumor immune microenvironment analysis. A nomogram incorporating PCDI and clinicopathological characteristics was established to predict the prognosis of LUAD patients. Forty PCD-associated DEGs related to LUAD were obtained by WGCNA analysis and differential expression analysis, followed by unsupervised clustering to identify two LUAD molecular subtypes. A programmed cell death index (PCDI) with a five-gene signature was established by machine learning algorithms. LUAD patients were then divided into a high PCDI group and a low PCDI group using the median PCDI as a cutoff. Survival and therapeutic analysis revealed that the high PCDI group had a poor prognosis and was more sensitive to targeted drugs but less sensitive to immunotherapy compared to the low PCDI group. Further enrichment analysis showed that B cell-related pathways were significantly downregulated in the high PCDI group. Accordingly, the decreased tumor immune cell infiltration and the lower tumor tertiary lymphoid structure (TLS) scores were also found in the high PCDI group. Finally, a nomogram with reliable predictive performance PCDI was constructed by incorporating PCDI and clinicopathological characteristics, and a user-friendly online website was established for clinical reference ( https://nomogramiv.shinyapps.io/NomogramPCDI/ ). We performed the first comprehensive analysis of the clinical relevance of genes regulating 13 PCD patterns in LUAD and identified two LUAD molecular subtypes with distinct PCD-related gene signature which indicated differential prognosis and treatment sensitivity. Our study provided a new index to predict the efficacy of therapeutic interventions and the prognosis of LUAD patients for guiding personalized treatments.

Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features

To investigate whether the combination scheme of deep learning score (DL-score) and radiomics can improve preoperative diagnosis in the presence of micropapillary/solid (MPP/SOL) patterns in lung adenocarcinoma (ADC). A retrospective cohort of 514 confirmed pathologically lung ADC in 512 patients after surgery was enrolled. The clinicoradiographic model (model 1) and radiomics model (model 2) were developed with logistic regression. The deep learning model (model 3) was constructed based on the deep learning score (DL-score). The combine model (model 4) was based on DL-score and R-score and clinicoradiographic variables. The performance of these models was evaluated with area under the receiver operating characteristic curve (AUC) and compared using DeLong's test internally and externally. The prediction nomogram was plotted, and clinical utility depicted with decision curve. The performance of model 1, model 2, model 3 and model 4 was supported by AUCs of 0.848, 0.896, 0.906, 0.921 in the Internal validation set, that of 0.700, 0.801, 0.730, 0.827 in external validation set, respectively. These models existed statistical significance in internal validation (model 4 vs model 3, P = 0.016; model 4 vs model 1, P = 0.009, respectively) and external validation (model 4 vs model 2, P = 0.036; model 4 vs model 3, P = 0.047; model 4 vs model 1, P = 0.016, respectively). The decision curve analysis (DCA) demonstrated that model 4 predicting the lung ADC with MPP/SOL structure would be more beneficial than the model 1and model 3 but comparable with the model 2. The combined model can improve preoperative diagnosis in the presence of MPP/SOL pattern in lung ADC in clinical practice.

Deep learning-based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma.

Classification of histological patterns in lung adenocarcinoma (LUAD) is critical for clinical decision-making, especially in the early stage. However, the inter- and intraobserver subjectivity of pathologists make the quantification of histological patterns varied and inconsistent. Moreover, the spatial information of histological patterns is not evident to the naked eye of pathologists. We establish the LUAD-subtype deep learning model (LSDLM) with optimal ResNet34 followed by a four-layer Neural Network classifier, based on 40 000 well-annotated path-level tiles. The LSDLM shows robust performance for the identification of histopathological subtypes on the whole-slide level, with an area under the curve (AUC) value of 0.93, 0.96 and 0.85 across one internal and two external validation data sets. The LSDLM is capable of accurately distinguishing different LUAD subtypes through confusion matrices, albeit with a bias for high-risk subtypes. It possesses mixed histology pattern recognition on a par with senior pathologists. Combining the LSDLM-based risk score with the spatial K score (K-RS) shows great capacity for stratifying patients. Furthermore, we found the corresponding gene-level signature (AI-SRSS) to be an independent risk factor correlated with prognosis. Leveraging state-of-the-art deep learning models, the LSDLM shows capacity to assist pathologists in classifying histological patterns and prognosis stratification of LUAD patients.

Analysis of m6A modulator-mediated methylation modification patterns and the tumor microenvironment in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. In the development and progression of LUAD, epigenetic aberration plays a crucial role. However, the function of RNA N6-methyladenosine (m6A) modifications in the LUAD progression is unknown. The m6A regulator modification patterns in 955 LUAD samples were analyzed comprehensively. Patterns were systematically correlated with the tumor microenvironment (TME) cell-infiltration characteristics. Using principal component analysis algorithms, the m6Ascore was generated to quantify m6A modification patterns in individual tumors. Then, their values for predicting prognoses and therapeutic response in LUAD patients were assessed. Three distinct m6A modification patterns in LUAD were identified. Among them, the prognosis of m6Acluster C was the best, while the prognosis of m6Acluster A was the worst. Interestingly, the characterization of TME cell infiltration and biological behavior differed among the three patterns. To evaluate m6A modification patterns within individual tumors, an m6Ascore signature was constructed. The results showed that the high m6Ascore group was associated with a better prognosis; tumor somatic mutations and tumor microenvironment differed significantly between the high- and low- m6Ascore groups. Furthermore, in the cohort with anti-CTLA-4 treatment alone, patients with a high m6Ascore had higher ICI scores, which indicated significant therapeutic advantage and clinical benefits.

Comprehensive analyses of one-carbon metabolism related genes and their association with prognosis, tumor microenvironment, chemotherapy resistance and immunotherapy in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer and is a global public health concern. One-carbon (1C) metabolism plays a crucial role in the occurrence and development of multiple cancer types. However, there are limited studies investigating 1C metabolism in LUAD. This study aims to evaluate the prognostic value of 1C metabolism-related genes in LUAD and to explore the potential correlation of these genes with gene methylation, the tumor microenvironment, and immunotherapy. Methods: We identified 26 1C metabolism-related genes and performed a Kaplan-Meier and Cox regression analysis to evaluate the prognostic value of these genes. Consensus clustering was further performed to determine the 1C metabolism-related gene patterns in LUAD. The clinical and molecular characteristics of subgroups were investigated based on consensus clustering. CIBERSORT and ssGSEA algorithms were used to calculate the relative infiltration levels of multiple immune cell subsets. The relationship between 1C metabolism-related genes and drug sensitivity to immunotherapy was evaluated using the CellMiner database and IMvigor210 cohort, respectively. Results: The expression levels of 23 1C metabolism-related genes were significantly different between LUAD tumor tissues and normal tissues. Seventeen of these genes were related to prognosis. Two clusters (cluster 1 and cluster 2) were identified among 497 LUAD samples based on the expression of 7 prognosis-related genes. Distinct expression patterns were observed between the two clusters. Compared to cluster 2, cluster 1 was characterized by inferior overall survival (OS) (median OS = 41 vs. 60months, p = 0.00031), increased tumor mutation burden (15.8 vs. 7.5mut/Mb, p < 0.001), high expression of PD-1 (p < 0.001) and PD-L1 (p < 0.001), as well as enhanced immune infiltration. 1C metabolism-related genes were positively correlated with the expression of methylation enzymes, and a lower methylation level was observed in cluster 1 (p = 0.0062). Patients in cluster 1 were resistant to chemotherapy drugs including pemetrexed, gemcitabine, paclitaxel, etoposide, oxaliplatin, and carboplatin. The specific expression pattern of 1C metabolism-related genes was correlated with a better OS in patients treated with immunotherapy (median OS: 11.2 vs. 7.8months, p = 0.0034). Conclusion: This study highlights that 1C metabolism is correlated with the prognosis of LUAD patients and immunotherapy efficacy. Our findings provide novel insights into the role of 1C metabolism in the occurrence, development, and treatment of LUAD, and can assist in guiding immunotherapy for LUAD patients.

Characterization of the evolution trajectory and immune profiling of new histologic patterns in lung adenocarcinoma.

In lung adenocarcinoma (LUAD), the appearance of morphologically diverse tumor regions, termed histological patterns, is closely associated with disease progression and lymph node metastasis. However, the molecular characteristics of the histological patterns in LUAD and the underlying molecular evolutionary mechanisms between the histological patterns in primary tumors and lymph node metastases are poorly understood. Here, we re-analyzed the large TCGA-LUAD dataset and depicted a comprehensive profiling of the genome and transcriptome across the histological patterns in LUAD. Tumor phylogenetic trajectory analysis suggested that the complex glands is more apt to metastasize to the lymph node. Further deconvolution of the tumor microenvironment demonstrated that the complex glands had a higher infiltration of cancer-associated fibroblasts (CAFs). Single-cell transcriptome profiling of complex glands pattern identified a novel CAF subtype co-expressing fibroblast activation protein-alpha (FAP) and stimulator of interferon genes (STING). Moreover, our data demonstrated that FAP is an important downstream effector of STING in CAFs. In summary, our results provide the basis for the development of innovative therapeutic guidelines and intervention strategies for LUAD patients.

Clinicopathological and CT features of tumor spread through air space in invasive lung adenocarcinoma.

ObjectiveTumor spread through air spaces (STAS) has recently been reported as a novel invasive pattern in lung adenocarcinoma. Thus, this study aimed to investigate the clinicopathological and radiological features in invasive lung adenocarcinoma with tumor STAS.MethodsData of 503 invasive lung adenocarcinoma patients who underwent surgery between 1 January 2015 and 31 December 2021 were collected. The correlations between STAS presence and clinicopathological and radiological characteristics were analyzed. Statistical analysis was performed using SPSS 22.0.ResultsAmong the 503 patients with invasive adenocarcinoma, 247 (47.9%) and 262 (52.1%) patients were positive and negative for STAS, respectively. Compared to STAS-negative adenocarcinoma, STAS was more common in papillary, micropapillary, and solid tumors (p < 0.01); STAS was associated with advanced pT (p = 0.024), pN (p < 0.001), and pTNM (p < 0.001) stage, more lymph node metastases (p < 0.01), more pleural invasion (p < 0.01), and more neurovascular invasion (p = 0.025). The maximum diameter (p < 0.01), the maximum diameters of the solid component (p < 0.01), and the consolidation/tumor ratio (CTR, p < 0.01) were significantly larger in STAS-positive than in STAS-negative adenocarcinoma. Other common computed tomography (CT) features of adenocarcinomas, i.e., lobulation (p < 0.01), spiculation (p < 0.01), vacuole (p < 0.01), air bronchogram (p = 0.020), vascular convergence (p < 0.01), and pleural indentation (p < 0.01) were significantly associated with STAS. In a multivariable analysis, the maximal diameter of the solid component (odds ratio [OR], 2.505; 95% confidence interval [CI], 1.886–3.329), vacuole (OR, 3.301; 95% CI, 1.822–5.980), and spiculation (OR, 2.162; 95% CI, 1.221–3.829) were independent predictors of STAS. The area under the curve (AUC) of the maximal diameter of the solid component was 0.757 (95% CI, 0.714–0.799; p < 0.001), the sensitivity was 73.9%, and the specificity was 69.1% at a cutoff value of 1.18 cm.ConclusionSTAS was significantly correlated with several invasive clinicopathological and radiological characteristics, and the maximal diameter was an independent predictor of STAS. These results will prove helpful in identifying STAS-positive adenocarcinoma by CT before surgical resection.

Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a complex tumor microenvironment. Neddylation, as a type of post-translational modification, plays a vital role in the development of LUAD. To date, no study has explored the potential of neddylation-associated genes for LUAD classification, prognosis prediction, and treatment response evaluation. Methods: Seventy-six neddylation-associated prognostic genes were identified by Univariate Cox analysis. Patients with LUAD were classified into two patterns based on unsupervised consensus clustering analysis. In addition, a 10-gene prognostic signature was constructed using LASSO-Cox and a multivariate stepwise regression approach. Results: Substantial differences were observed between the two patterns of LUAD in terms of prognosis. Compared with neddylation cluster2, neddylation cluster1 exhibited low levels of immune infiltration that promote tumor progression. Additionally, the neddylation-related risk score correlated with clinical parameters and it can be a good predictor of patient outcomes, gene mutation levels, and chemotherapeutic responses. Conclusion: Neddylation patterns can distinguish tumor microenvironment and prognosis in patients with LUAD. Prognostic signatures based on neddylation-associated genes can predict patient outcomes and guide personalized treatment.

P2.09-01 MRI-based Radiomic Signature to Predict Pathologic High-grade Pattern in Lung Adenocarcinoma

While curative resection is the established treatment in N0 early lung adenocarcinoma patients, recurrence have been reported to be more than 30% and one of the risk factors can be attributed to micropapillary and solid pattern in histology, both being high-grade patterns associated with adverse outcome. Also, incorporation of high-grade pattern in lung adenocarcinoma grading system yields good correlation with prognosis. In this regard, our study focuses on developing the first prediction model to find quantitative MRI features of micropapillary or solid pattern (MPsol) in lung adenocarcinoma by radiomics approach.

Comprehensive analyses unveil novel genomic and immunological characteristics of micropapillary pattern in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) usually contains heterogeneous histological subtypes, among which the micropapillary (MIP) subtype was associated with poor prognosis while the lepidic (LEP) subtype possessed the most favorable outcome. However, the genomic features of the MIP subtype responsible for its malignant behaviors are substantially unknown. In this study, eight FFPE samples from LUAD patients were micro-dissected to isolate MIP and LEP components, then sequenced by whole-exome sequencing. More comprehensive analyses involving our samples and public validation cohorts on the two subtypes were performed to better decipher the key biological and evolutionary mechanisms. As expected, the LEP and MIP subtypes exhibited the largest disease-free survival (DFS) differences in our patients. EGFR was found with the highest mutation frequency. Additionally, shared mutations were observed between paired LEP and MIP components from single patients, and recurrent mutations were verified in the Lung-Broad, Lung-OncoSG, and TCGA-LUAD cohorts. Distinct biological processes or pathways were involved in the evolution of the two components. Besides, analyses of copy number variation (CNV) and intratumor heterogeneity (ITH) further discovered the possible immunosurveillance escape, the discrepancy between mutation and CNV level, ITH, and the pervasive DNA damage response and WNT pathway gene alternations in the MIP component. Phylogenetic analysis of five pairs of LEP and MIP components further confirmed the presence of ancestral EGFR mutations. Through comprehensive analyses combining our samples and public cohorts, PTP4A3, NAPRT, and RECQL4 were identified to be co-amplified. Multi-omics data also demonstrated the immunosuppression prevalence in the MIP component. Our results uncovered the evolutionary pattern of the concomitant LEP and MIP components from the same patient that they were derived from the same initiation cells and the pathway-specific mutations acquired after EGFR clonal mutation could shape the subtype-specificity. We also confirmed the immunosuppression prevalence in the MIP subtype by multi-omics data analyses, which may have resulted in its unfavorable prognosis.

Crosstalk of Eight Types of RNA Modification Regulators Defines Tumor Microenvironments, Cancer Hallmarks, and Prognosis of Lung Adenocarcinoma

RNA modification has become an exciting underexplored field in recent years. In lung adenocarcinoma (LUAD), m6A was the best characterized and most studied RNA modification, while knowledge about other kinds of RNA modifications in LUAD is limited. In our study, we included a total of 100 RNA modification regulators of eight types of cancer-related RNA modifications (m6A, m1A, m5C, Nm, m7G, Ψ, A-to-I, and mcm5s2U) to systematically profile their specific roles in LUAD. By gene mutation and expression analysis, we identified extensive dysregulations and complicated interactions of 100 RNA modification regulators in LUAD. Based on unsupervised clustering analysis, gene set variation analysis (GSVA), and single-sample gene-set enrichment analysis (ssGSEA), two RNA modification patterns in LUAD were defined to show distinct biological characteristics. The favorable prognostic pattern was enriched with infiltrated immune cells, including activated B cells, CD8 T cells, eosinophil cells, dendritic cells, and natural killer cells, while the unfavorable prognostic pattern was enriched with cancer hallmarks, including hypoxia, epithelial-mesenchymal transition (EMT), angiogenesis, PI3K-AKT-mTOR pathway, MYC pathway, and glycolysis pathway. We also constructed an RNA modification score (RMScore) based on five critical genes (CYP17A1, NTSR1, PITX3, KRT6A, and ANLN) to evaluate the RNA modification status of individual LUAD patients. RMScore was revealed to be related to the infiltrated immune cells and cancer hallmarks and was an independent prognostic factor in the TCGA-LUAD cohort and two external GEO-LUAD cohorts. Our study was the first to comprehensively investigate the dysregulations, crosstalk, and potential prognostic value of eight types of RNA modifications in LUAD. Our results highlighted the significance of eight types of RNA modifications in tumor microenvironments and cancer hallmarks and provided novel prognostic biomarkers and potential therapeutic targets in the management of LUAD patients in the future.

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma.

ObjectivesSpread through air spaces (STAS), a new invasive pattern in lung adenocarcinoma (LUAD), is a risk factor for poor outcome in early-stage LUAD. This study aimed to develop and validate a CT-based radiomics model for predicting STAS in stage IA LUAD.MethodsA total of 395 patients (169 STAS positive and 226 STAS negative cases, including 316 and 79 patients in the training and test sets, respectively) with stage IA LUAD before surgery were retrospectively included. On all CT images, tumor size, types of nodules (solid, mix ground-glass opacities [mGGO] and pure GGO [pGGO]), and GGO percentage were recorded. Region of interest (ROI) segmentation was performed semi-automatically, and 1,037 radiomics features were extracted from every segmented lesion. Intraclass correlation coefficients (ICCs), Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were used to filter unstable (ICC < 0.75) and redundant features (r > 0.8). A temporary model was established by multivariable logistic regression (LR) analysis based on selected radiomics features. Then, seven radiomics features contributing the most were selected for establishing the radiomics model. We then built two predictive models (clinical-CT model and MixModel) based on clinical and CT features only, and the combination of clinical-CT and Rad-score, respectively. The performances of these three models were assessed.ResultsThe radiomics model achieved good performance with an area under of curve (AUC) of 0.812 in the training set, versus 0.850 in the test set. Furthermore, compared with the clinical-CT model, both radiomics model and MixModel showed higher AUC and better net benefit to patients in the training and test cohorts.ConclusionThe CT-based radiomics model showed satisfying diagnostic performance in early-stage LUAD for preoperatively predicting STAS, with superiority over the clinical-CT model.

Comprehensive analysis of mutational profile and prognostic significance of complex glandular pattern in lung adenocarcinoma.

BackgroundComplex glandular pattern (CGP) was included as high-grade pattern in the new grading system proposed by The International Association for the Study of Lung Cancer. We aimed to investigate the mutational profile and validate the prognostic significance and proper cut-off value to distinguish the aggressive behavior of CGP.MethodsCGP was defined as nests of tumor cells with sieve-like perforation, fused glands with irregular borders or back-to-back glands without intervening stroma. Patients were categorized into four groups according to the percentage of CGP component (0%, 1–19%, 20–49%, 50–100%). Cox’s proportional hazards model was applied to analyze recurrence free survival (RFS) and overall survival (OS).ResultsA total of 950 patients with resected lung adenocarcinoma was enrolled. The most frequent driver mutation in this cohort was EGFR and was detected in 624 (65.7%) patients. EGFR mutation was more frequently observed in patients with <20% CGP than in patients with ≥20% CGP (73.6% vs. 60.2%), while KRAS mutation and ALK rearrangement was significantly associated with ≥20% CGP. Patients with 20% or greater CGP exhibited significant worse RFS (P<0.001) and OS (P<0.001) than their counterparts. Moreover, the multivariate Cox regression analysis confirmed that CGP (≥20%) was a risk factor for a worse RFS (P=0.001) and OS (P<0.001) independent of staging and gene mutation. Smaller portion of CGP (<20%) were comparable in RFS and OS to those without CGP (0%). There was also no significant difference in RFS and OS between the 20–49% and ≥50% group.ConclusionsOur study provided mutational profile of patients with different CGP, validated CGP as a negative prognostic factor and provided extra evidences for the optimal cut-off value of CGP percentage.

Abstract LB504: Automated grading of growth patterns in lung adenocarcinoma-from TRACERx to LATTICe-A

Abstract Lung adenocarcinoma exhibits distinct growth patterns (WHO, 2021) and the International Association for the Study of Lung Cancer (IASLC) grading system, based on the nature and proportion of histologic subtypes, is highly prognostic (Moreira et al. 2020). However, both recognition and quantification of growth patterns suffer from high interobserver variability among pathologists. Here, we present a large-scale application to automate the segmentation of histologic patterns and reproduce the IASLC grading system to stratify patients and predict prognosis. A deep learning model was trained to recognize and segment 6 histologic patterns (lepidic, acinar, papillary, micropapillary, solid, and cribriform) on 49 whole-slide images from TRACERx (AbdulJabbar et al. 2020). This model was directly applied to an independent cohort, consisting of 4324 hematoxylin and eosin-stained sections from 970 patients (the Leicester Archival Thoracic Tumor Investigatory Cohort, Moore et al. 2019). Growth pattern segmentation performance was first evaluated against 2433 hand annotations covering 6 patterns from 9 images at a pixel level using Dice coefficient. The average Dice was 0.502. Predicted predominant pattern per tumor was then compared to a subspecialty pathologist, achieving an overall agreement (51%), comparable to the interobserver rate among pathologists (52%, Boland et al. 2017). Discordant cases were more heterogeneous (p=4.5e-10, Shannon diversity based on pathological scores), underscoring the challenges posed by intratumor heterogeneity. Patients with a high proportion of micropapillary in the tumor, identified by deep learning, had significantly worse relapse-free survival (RFS) in multivariate analyses including clinical parameters (p=0.00127, Hazard Ratio (HR)=6.4, 95% confidence interval (CI)=2.07-19.8, n=827), consistent with previous publication (Cha et al. 2014). The Kaplan-Meier curve for RFS was significantly differentiated with both automated and pathological IASLC grading (p&amp;lt;0.0001). Moreover, patients with predominantly high-grade patterns (solid, micropapillary, cribriform) identified by deep learning had significantly reduced RFS (p=6.22e-4, HR=1.5, 95% CI=1.18-1.8, n=970). The prognostic effect was stronger using IASLC grading (cutoff: 20%, p=5.56e-6, HR=1.7, 95% CI=1.36-2.2), comparable to the pathological score at IASLC grade 3 (cutoff: 20%, p=2.41e-5, HR=1.8, 95% CI=1.35-2.3). A similar performance was observed for overall survival regarding above analyses. To the best of our knowledge, this study represents the largest application of deep learning to recognize tumor growth patterns in lung adenocarcinoma. Histologically heterogeneous growth patterns can be automatically identified using a method trained on an independent cohort. Automated tumor grading is significantly associated with patient outcomes, supporting its potential clinical utility. Citation Format: Xiaoxi Pan, Khalid AbdulJabbar, Jose Coelho-Lima, Anca-Ioana Grapa, Hanyun Zhang, Ho Kwan Alvin Cheung, Sarah J. Aitken, David A. Moore, Charles Swanton, John Le Quesne, Yinyin Yuan. Automated grading of growth patterns in lung adenocarcinoma-from TRACERx to LATTICe-A [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB504.

The Identification of Two RNA Modification Patterns and Tumor Microenvironment Infiltration Characterization of Lung Adenocarcinoma.

Background: RNA modification plays an important role in many diseases. A comprehensive study of tumor microenvironment (TME) characteristics mediated by RNA modification regulators will improve the understanding of TME immune regulation. Methods: We selected 26 RNA modification “writers” of lung adenocarcinoma (LUAD) samples and performed unsupervised clustering analysis to explore RNA modification patterns in LUAD. Differentially expressed genes (DEGs) with RNA modification patterns were screened to develop a “writers” of RNA modification score (WM score) system. The infiltration ratio of TME cell subsets was analyzed by CIBERSORT. Results: We identified two RNA modification modes showing different characteristics of overall survival (OS) and TME cell infiltration. According to WM score, LUAD patients were divided into a high-WM score group and a low-WM score group. High-scored patients had a poor prognosis and higher tumor mutation burden (TMB), they were more sensitive to four LUAD therapies (erlotinib, XA V939, gefitinib, and KU-55933) and more clinically responsive to PD-L1 treatment. Those with a low WM score showed higher stromal scores, ESTIMATE scores, and survival chance. Conclusion: Our work revealed the potential role of RNA modification patterns in TME, genetic variation, targeted inhibitor therapy, and immunotherapy. Identifying RNA modification pattern of LUAD patients help understand the characteristics of TME and may promote the development of immunotherapy strategies.

Prognostic implication of tumor spread through air spaces in patients with pathologic N0 lung adenocarcinoma

ObjectivesTumor spread through air spaces (STAS) has been identified as an invasive pattern in lung adenocarcinoma (ADC), but the prognostic implication of STAS has not been well studied in patients with pathologic N0 lung ADC. The purpose of this study was to evaluate the prognostic implication of STAS in pathologic N0 lung ADC patients after radical surgery. Materials and methodsBetween January 2017 and December 2018, 796 patients with completely resected pathologic N0 lung ADC were reviewed. Pearson’s chi-square test or Fisher exact test was used for comparing the relationship between STAS and clinicopathological features. The log-rank test and multivariate Cox regression models were used to explore prognostic factors. ResultsAmong the 796 patients, STAS was positive in 201 patients (25.3%). The presence of STAS was significantly associated with patients with solid nodules (P < 0.001), micropapillary pattern-predominant adenocarcinoma/solid pattern-predominant adenocarcinoma (P < 0.001), larger tumor size (P < 0.001), visceral pleural invasion (P < 0.001) and lymphovascular invasion (P < 0.001). Multivariable analysis showed that STAS was an independent prognostic factor for recurrence-free survival (RFS) in pathologic N0 lung ADC patients (P = 0.014). For patients with acinar pattern-predominant adenocarcinoma (APA) / papillary pattern-predominant adenocarcinoma (PPA) / invasive mucinous adenocarcinoma (IMA) and patients who underwent lobectomy, STAS was an independent prognostic factor for RFS (P = 0.015, P = 0.011; respectively) and overall survival (OS) (P = 0.038, P = 0.020; respectively). ConclusionIn this study, STAS was an independent prognostic factor for RFS in pathologic N0 lung adenocarcinomas, and it was also an independent prognostic factor for RFS and OS in patients with APA/PPA/IMA and those who received lobectomy.

Preoperative CT-Based Radiomics Combined With Nodule Type to Predict the Micropapillary Pattern in Lung Adenocarcinoma of Size 2cm or Less: A Multicenter Study.

PurposeTo construct an optimal radiomics model for preoperative prediction micropapillary pattern (MPP) in adenocarcinoma (ADC) of size ≤ 2 cm, nodule type was used for stratification to construct two radiomics models based on high-resolution computed tomography (HRCT) images.Materials and MethodsWe retrospectively analyzed patients with pathologically confirmed ADC of size ≤ 2 cm who presented to three hospitals. Patients presenting to the hospital with the greater number of patients were included in the training set (n = 2386) and those presenting to the other two hospitals were included in the external validation set (n = 119). HRCT images were used for delineation of region of interest of tumor and extraction of radiomics features; dimensionality reduction was performed for the features. Nodule type was used to stratify the data and the random forest method was used to construct two models for preoperative prediction MPP in ADC of size ≤ 2 cm. Model 1 included all nodule types and model 2 included only solid nodules. The receiver operating characteristic curve was used to assess the prediction performance of the two models and independent validation was used to assess its generalizability.ResultsBoth models predicted ADC with MPP preoperatively. The area under the curve (AUC) of prediction performance of models 1 and 2 were 0.91 and 0.78, respectively. The prediction performance of model 2 was lower than that of model 1. The AUCs in the external validation set were 0.81 and 0.72, respectively. The DeLong test showed statistically significant differences between the training and validation sets in model 1 (p = 0.0296) with weak generalizability. There was no statistically significant difference between the training and validation sets in model 2 (p = 0.2865) with some generalizability.ConclusionNodule type is an important factor that affects the performance of radiomics predictor model for MPP with ADC of size ≤ 2 cm. The radiomics prediction model constructed based on solid nodules alone, can be used to evaluate MPP and may contribute to proper surgical planning in patients with ADC of size ≤ 2 cm.